AIMC Topic: Fluorescein Angiography

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Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Jan 14, 2020
PURPOSE: To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs).
Humans Diabetic Retinopathy Neural Networks, Computer Algorithms Deep Learning Image Processing, Computer-Assisted Fundus Oculi Retina Fluorescein Angiography Female Aged Male Middle Aged
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Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.

Ophthalmology Dec 23, 2019
PURPOSE: To validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2).
Retinal Telangiectasis Treatment Outcome Drug Implants Visual Acuity Retinal Photoreceptor Cell Inner Segment Tomography, Optical Coherence Retinal Photoreceptor Cell Outer Segment Visual Fields Visual Field Tests Fluorescein Angiography Ciliary Neurotrophic Factor Retinal Vessels Female Reproducibility of Results Male Deep Learning Humans
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Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Nov 4, 2019
PURPOSE: To investigate the feasibility of training an artificial intelligence (AI) on a public-available AI platform to diagnose polypoidal choroidal vasculopathy (PCV) using indocyanine green angiography (ICGA).
Retrospective Studies ROC Curve Choroid Tomography, Optical Coherence Fundus Oculi Humans Artificial Intelligence Machine Learning Fluorescein Angiography Polyps Choroid Diseases
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DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images.

Medical hypotheses Oct 10, 2019
Recent studies have shown that convolutional neural networks (CNNs) can be more accurate, efficient and even deeper on their training if they include direct connections from the layers close to the input to those close to the output in order to trans...
Image Processing, Computer-Assisted Area Under Curve Fundus Oculi Fluorescein Angiography Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning ROC Curve Retinal Vessels Humans Algorithms
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Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.

Clinical & experimental ophthalmology Jul 25, 2019
IMPORTANCE: Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision.
False Positive Reactions Fluorescein Angiography Wet Macular Degeneration Photography Ophthalmologists Choroidal Neovascularization Visual Acuity Predictive Value of Tests Area Under Curve Fundus Oculi Middle Aged Diagnosis, Computer-Assisted Deep Learning ROC Curve Humans Algorithms Reproducibility of Results Retrospective Studies Sensitivity and Specificity Female Male
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Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

PloS one Jun 6, 2019
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-impl...
Image Processing, Computer-Assisted Fundus Oculi Fluorescein Angiography Female Humans Male Diabetic Retinopathy Deep Learning Databases, Factual India
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Automated OCT angiography image quality assessment using a deep learning algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie May 22, 2019
PURPOSE: To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).
Humans Fluorescein Angiography Retinal Vessels Neural Networks, Computer Deep Learning Algorithms Tomography, Optical Coherence Retrospective Studies Fundus Oculi Reproducibility of Results Artifacts
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Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.

Ophthalmology. Glaucoma Apr 1, 2019
PURPOSE: To validate a deep residual learning algorithm to diagnose glaucoma from fundus photography using different fundus cameras at different institutes.
Cross-Sectional Studies Male Deep Learning ROC Curve Fundus Oculi Optic Disk Fluorescein Angiography Glaucoma Algorithms Female Humans Aged
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Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Aug 8, 2018
PURPOSE: To automatically detect and classify geographic atrophy (GA) in fundus autofluorescence (FAF) images using a deep learning algorithm.
Tomography, Optical Coherence Humans Fundus Oculi Neural Networks, Computer Healthy Volunteers Geographic Atrophy Fluorescein Angiography Male Machine Learning Female Algorithms Middle Aged Reproducibility of Results Disease Progression Aged Diagnosis, Computer-Assisted Sensitivity and Specificity
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Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

Molecules (Basel, Switzerland) Nov 23, 2017
The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpf...
Image Processing, Computer-Assisted Fundus Oculi Retina Fluorescein Angiography Humans Diabetic Retinopathy Neural Networks, Computer Algorithms
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